


import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DPTImageProcessor




class       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=7       ,  lowercase_=3       ,  lowercase_=18       ,  lowercase_=30       ,  lowercase_=400       ,  lowercase_=True       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=[0.5, 0.5, 0.5]       ,  )  -> Optional[Any]:
											a__      =size if size is not None else {'height': 18, 'width': 18}
											a__      =parent
											a__      =batch_size
											a__      =num_channels
											a__      =image_size
											a__      =min_resolution
											a__      =max_resolution
											a__      =do_resize
											a__      =size
											a__      =do_normalize
											a__      =image_mean
											a__      =image_std





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}







@require_torch
@require_vision
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =DPTImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =DPTImageProcessingTester(self)





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return self.image_processor_tester.prepare_image_processor_dict()





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(lowercase_       ,  'image_mean'))
											self.assertTrue(hasattr(lowercase_       ,  'image_std'))
											self.assertTrue(hasattr(lowercase_       ,  'do_normalize'))
											self.assertTrue(hasattr(lowercase_       ,  'do_resize'))
											self.assertTrue(hasattr(lowercase_       ,  'size'))





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size       ,  {'height': 18, 'width': 18})

											a__      =self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42)
											self.assertEqual(image_processor.size       ,  {'height': 42, 'width': 42})





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape       ,  (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )       ,  )

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape       ,  (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )       ,  )





				def 						__UpperCamelCase    (				self)  -> int:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  numpify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  np.ndarray)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape       ,  (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )       ,  )

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape       ,  (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )       ,  )





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  torchify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  torch.Tensor)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape       ,  (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )       ,  )

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape       ,  (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )       ,  )





"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}







class    __a						( __snake_case							):
				lowerCamelCase     :       Union[str, Any]			       ='open-llama'







				def __init__( self      ,	UpperCAmelCase=10_0000      ,	UpperCAmelCase=4096      ,	UpperCAmelCase=1_1008      ,	UpperCAmelCase=32      ,	UpperCAmelCase=32      ,	UpperCAmelCase="silu"      ,	UpperCAmelCase=2048      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=1E-6      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0      ,	UpperCAmelCase=1      ,	UpperCAmelCase=2      ,	UpperCAmelCase=False      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=None      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			max_position_embeddings
					lowerCAmelCase_      							=			hidden_size
					lowerCAmelCase_      							=			intermediate_size
					lowerCAmelCase_      							=			num_hidden_layers
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			rms_norm_eps
					lowerCAmelCase_      							=			use_cache
					lowerCAmelCase_      							=			kwargs.pop(
					    '''use_memorry_efficient_attention'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			hidden_dropout_prob
					lowerCAmelCase_      							=			attention_dropout_prob
					lowerCAmelCase_      							=			use_stable_embedding
					lowerCAmelCase_      							=			shared_input_output_embedding
					lowerCAmelCase_      							=			rope_scaling
					self._rope_scaling_validation()

					super().__init__(
					    pad_token_id=UpperCAmelCase      ,	bos_token_id=UpperCAmelCase      ,	eos_token_id=UpperCAmelCase      ,	tie_word_embeddings=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.rope_scaling is None:
						return

					if not isinstance(self.rope_scaling      ,	UpperCAmelCase   ) or len(self.rope_scaling   ) != 2:
						raise ValueError(
						    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
						    F"""got {self.rope_scaling}"""   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''type'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''factor'''      ,	UpperCAmelCase   )
					if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""   )
					if rope_scaling_factor is None or not isinstance(UpperCAmelCase      ,	UpperCAmelCase   ) or rope_scaling_factor <= 1.0:
						raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""   )